A survey was conducted to obtain a greater understanding of the costs involved in operating a biobank. The anonymized survey data was then used to develop the BEMT, a cost modeling tool for biobanks. Users of the tool will be able to create a cost profile for their biobanks' specimens, products and services, establish pricing, and allocate costs for biospecimens based on percent cost recovered, and perform project-specific cost analyses and financial forecasting.

The US National Science Foundation's Long Term Ecological Research (LTER) network was established in 1980 to provide the scientific expertise, research platforms, and long-term data sets necessary to document and analyze environmental change (http://www.lternet.edu). There are currently 25 sites in the US LTER network representing a range of ecosystems, including deserts, prairies, forests, tundra, lakes, urban areas, estuaries, coastal reefs, the pelagic ocean, and production agriculture. Although the research questions being addressed vary across the network, each site collects data on primary production, population dynamics, the cycling of both organic and inorganic matter, and disturbance patterns. Long-term data in these core areas enable changes in critical ecological processes to be tracked over time and facilitate comparisons among different ecosystem types

Tests of models on independent data as a part of the model development often end when the errors are calculated and evaluated, and compared with corresponding results from previously developed models. The error calculations, however, provide only limited information with respect to the application of a model. Valuable information may also be obtained if a link between the errors, the consequential incorrect treatment decisions and the corresponding economic losses can be established. A case study in Norway, based on models for prediction of basal area mean diameter and number of trees/ha in forest stands, where the economic losses related to incorrect timing of final harvests when decisions were based on erroneous predicted information, is presented. The case study demonstrates that calculation of the economic losses provides information that is useful as a supplement to considerations based on errors only when it comes to application of the models. The quantification of the economic losses, expressed in monetary terms, provides information that enables the modeller to relate effects of model errors directly to inventory costs. The variations in losses under different forest conditions also demonstrated a potential for reduced costs where there is a choice between use of the models when the economic losses are low and use of field measurements when the economic losses are high.

Growth and yield models have been developed for many tropical countries with the intention of supporting management decisions relating to the yield of timber from forests. These tools have, however, largely failed to deliver significant improvements in management practice. Forest managers and policy makers increasingly have to consider the supply of a much wider range of goods and services, including non-timber forest products and a range of environmental services. It is obvious that existing approaches for yield regulation, which have failed to deliver management improvements for the regulation of timber, will be even less suitable for application in systems of multiple objective forest management supporting the livelihoods of a wide range of stakeholders. New tools and approaches are required to support decision making for management and policy development. The multiple objective forest management (MOFORM) cluster of research (www.moform.org) aims to support the development of new knowledge and tools for the regulation of the yield of goods and services from tropical forests. This will involve the modification and extension of existing growth and yield prediction tools (models) to develop a yield regulation toolbox. This will include tools for the prediction, allocation, monitoring and reporting of yield from forests linked with appropriate economic and financial tools and instruments supporting multiple objective forest management. This chapter reports on the implementation of yield regulation pilot studies in Indonesia and Guyana utilizing two growth and yield models MYRLIN and SYMFOR to support forest management decisions and the development of policy. The lessons learned from these studies are discussed in terms of how to design tools and approaches that better meet the needs of forest managers and policy makers for multiple objective forest management.

Decisions regarding the management and monitoring of forests are often predicated on information obtained from models. The nature, scope, scale and purpose of models used for assessing forests are many and vary widely. Correspondingly, the mathematical and statistical tools used to develop and test such models are equally prodigious and diverse. Choosing a particular quantitative tool depends on a host of factors including the purpose for which the model is being developed, available data for parameter estimation, model scale (both spatial and temporal), inherent assumptions in the model, background and philosophical bent of the modeller, and potential users of the model. While some modelling tasks seem tailor-made for the application of a certain mathematical or statistical technique, other tasks seem not to lend themselves so obviously to the use of a particular quantitative tool. One arduous task for which adequate quantitative tools seem to be lacking is spanning scales at various resolutions. This chapter considers the task of modelling across scales and discusses some of the quantitative tools useful for that task. Particular attention is paid to the use of simulation and experimentation.